Explainable deep learning for Mango leaf disease recognition using AlexNet and grad-CAM
提出可解释深度学习框架，用AlexNet结合贝叶斯调参、微调和Grad-CAM实现芒果叶病十分类。
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GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture
GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture。Research Square
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An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability
An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability。Cureus Journal of Computer Science.
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AI-Driven Transformation in Sustainable Agriculture: A Systematic Review
系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。
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Hybrid explainable DeiT-based framework for plant disease classification and severity estimation
提出基于DeiT的混合可解释框架，实现植物病害分类与严重度估计。
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Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops(像素级可解释压力指数:田间作物病害严重程度量化的语义分割框架)
植物病害每年造成全球农业产量损失20%-40%、经济损失超2200亿美元。文章提出统一深度学习流水线,集成语义分割、基于回归的严重程度估计和病害分类,根据感染叶片面积比例将胁迫严重程度分为四档。在包含1641样本、六个类别的苹果树叶病害分割数据集上,采用MobileNetV2的U-Net表现最佳,像素准确率98.20%、mIoU 0.70、检测准确率99.41%、单张图像处理时间仅14.7毫秒,适合实时田间应用。计算出的严重程度指数与专家标注高度相关(r=0.968, R²=0.937),为自动化作物监测和决策支持提供可靠方案。[预印本]
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